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In Underwater Acoustic Sensor Networks (UASNs), optimizing energy efficiency and minimizing void occurrences in routing is paramount. Due to the energy constraints of sensor nodes, low-power transmission is essential for conserving energy. Previous research highlighted the effectiveness of clustering and routing to enhance energy efficacy in UASNs. Therefore, the clustering and routing processes can be considered as optimization problems that are nondeterministic polynomial-time (NP) hard. These challenges can be tackled through the application of machine learning algorithms and meta-heuristics. In this context, K-means clustering is employed to partition the network into clusters, designating the centroid as an ideal Cluster Head (CH) location. This ensures a one-hop proximity between the CH and cluster members, reducing transmitting power and enhancing network energy efficiency. Subsequently, a potential CH is selected using a marine predator optimization (MPA) algorithm based on the derived multi-objective fitness function. The MPA algorithm not only determines the optimal CH but also moves the elected CH to the K-means centroid location. Consequently, Autonomous Underwater Vehicles (AUVs) are utilized to collect and route packets from the CH to the Base Station (BS), minimizing the occurrence of void nodes and avoiding obstacle collisions. An optimal routing path for AUV is established through a way-point-based navigation scheme to achieve high packet reliability. Additionally, the proposed method (DCARo) dynamically determines the optimal number of clusters using the elbow method, ensuring scalability according to network size. Extensive simulations affirm the superiority of the DCARo across various performance metrics. [ABSTRACT FROM AUTHOR]

Coverage hole restoration and connectivity is a typical problem for underwater wireless sensor networks. In underwater applications like underwater oilfield reservoirs, undersea minerals and monitoring etc., where nodes face many hurdles and are unable to cover the required region during a natural disaster such as tsunami, flood, earthquakes, and environmental interference. It creates a coverage hole and consumes high energy with bad network quality. This problem considered as an NP-complete problem where a set of sensor nodes is required to identify the k-coverage hole and m-connectivity. In the literature, researchers have not focused on k-coverage hole restoration and m-connectivity issues during natural disasters and environmental interference. To mitigate this problem, we proposed priority-based coverage hole restoration and -connection using a whale optimization scheme to restore coverage holes and extract relevant information for the construction of undersea oilfield reservoirs, minerals, and mines. In this scheme, we identified the list of k-coverage holes and addressed autonomous underwater vehicles (AUVs) to place the additional mobile nodes in an appropriate coverage hole. A novel multi-objective function is formulated to obtain the optimal path for AUVs. Furthermore, while restoring coverage holes, we checked the connectivity of nodes. In the network, each node coordinated sleep scheduling with neighbor nodes to maintain energy efficiency. Performance evaluation of the proposed scheme shows better results than the existing schemes under different network scenarios which provide maximum coverage and connectivity, less energy consumption with a high convergence rate. [ABSTRACT FROM AUTHOR]

In underwater wireless sensor networks, routing play a vital role in selecting an optimal path for packet forwarding. In routing scheme, most of the existing work is suffering from both load balancing and void node issue. This is due to the environmental interference, overloaded data, energy depletion, random deployment and mobility of the nodes. However, it causes loss of packet, high energy depletion and bad network quality. We have resolved this issue by implementing load balancing and void healing routing using cuckoo search optimization (CSO) scheme. In this scheme, first we placed the parent node and identify their child node within the transmission range in each level of the network. Then, we applied load balancing with priority based packet forwarding to maintain the uneven distribution of the load and reduces the end-to-end delay. Next, void healing routing with CSO scheme is addressed to recover the convex and concave void issue in the network. A novel multi-objective fitness function is also formulated for selecting the optimal number of nodes. In packet routing, each child node is responsible for receiving the packets from their neighbor nodes and transferred to the parent node. After receiving the packets at parent node, autonomous underwater vehicle is used for collecting the relevant packets from each parent node through minimum travelling time and send towards the base station. The performance evaluation of proposed scheme shows better network quality, packet delivery ratio, less energy consumption and delay over the existing solutions. [ABSTRACT FROM AUTHOR]